Helping you move from AI ideas to working solution
Damovo helps turn AI potential into practical business solutions. We support you at every stage, from strategy and use case identification to proof of concept, implementation and ongoing optimisation. Every solution is designed to align with your systems, data and compliance needs.
How should organisations approach AI projects?
Organisations should begin AI projects by defining the desired business outcome, validating the use case, assessing data readiness, evaluating infrastructure requirements, establishing governance frameworks and planning for user adoption. A proof of value is especially important for customised AI solutions, while vendor-based AI capabilities can often be implemented faster through configuration.
Many AI projects fail because they start with technology instead of business value. Before choosing a model, platform or vendor, you need to answer five practical questions:
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What business outcome should AI improve?
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Is the use case valuable enough to justify the effort?
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Is the required data available, clean and secure?
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Can the solution integrate with existing systems?
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Who owns governance, compliance and lifecycle management?
This is especially important when AI is developed as a tailored solution.
Should you make or buy your AI solution?
Choose a “make” approach when you need control
In a “make” approach, organisations independently combine components such as large language models (LLMs), AI agents, and Model Context Protocol (MCP) integrations to create a customised end-to-end solution. This approach is suitable when the AI solution must be tailored to specific processes, data sources, security requirements or regulatory constraints. While this gives organisations more flexibility and control, it also demands a stronger proof of value, clear governance and a realistic view of the resources, risks and implementation effort involved.
Choose a “buy” approach when speed matters
A “buy” approach is suitable when AI capabilities are already available in a vendor platform and match the required use case. This approach can often remove the need for a separate proof of concept (PoC), as the core functionality is already exists. The focus then shifts from proving the technology, to validating the business value, configuring the solution, integrating it with existing systems and supporting user adoption.
Damovo supports both approaches
Damovo helps organisations assess both options and choose the right approach for each use case. We combine own AI solution capabilities with AI-enabled partner technologies. This allows organisations to choose the right delivery model per use case rather than forcing one standard approach.
What makes an AI project successful?
Successful AI projects depend on more than the model or platform you choose. They require a clear strategy, the right infrastructure, relevant skills, reliable data and an organisation that is ready to adopt new ways of working.
Before implementing AI, organisations should assess whether these necessary foundations are in place. This helps prevent isolated pilot projects that fail to scale, AI tools that lack user trust and adoption, or solutions that introduce new security, compliance or operational risks. A strong foundation enables organisations to move from experimentation to sustainable business value with greater confidence and control.
AI initiatives need a clear link to business outcomes to be successful. Rather than pursuing broad ambitions, organisations should focus on a small number of high-value use cases that can be prioritised, measured and scaled effectively.
A strong AI strategy defines what success looks like, identifies the processes that should be improved and establishes how value will be measured. It should also address governance, security, compliance and ethical principles from the outset, especially in regulated industries and in the context of frameworks such as the EU AI Act.
The organisational impact of AI should be planned for alongside the technology itself. This includes skills development, adapting operating models, and establishing clear accountability between business and IT teams.
AI should not be approached as a one-time technology project. Long term success requires measurable KPIs, continuous optimisation, and lifecycle management to ensure solutions remain effective, trusted and aligned with business objectives
AI solutions require infrastructure that can support reliable deployment and seamless integration into your core business processes. This includes compatibility with existing systems such as ERP, CRM, collaboration platforms and other critical business applications.
Organisations also need to ensure their infrastructure can support the ability to scale AI initiatives in the future and connect to cloud-based AI services where appropriate. Equally important is allocating enough time for piloting, employee training, change management and process adaptation. When these requirements are underestimated, AI initiatives often struggle to progress beyond isolated experiments and fail to deliver sustainable business value.
AI only creates value when your people know how to use it responsibly and effectively. Employees and leaders need a practical understanding of what AI can and cannot do, where it can improve daily work and where human judgement remains essential.
This doesn’t mean every employee needs to become an AI expert, but your teams should understand the relevant use cases, the limits of the technology, the risks of poor data quality and the requirements for secure and compliant use.
Training should be closely aligned with real business processes rather than focusing on AI concepts and theory. Employees need practical, hands-on experience with the tools and workflows that will become part of their day-to-day responsibilities.
Reliable data is one of the most important foundations for successful AI adoption. AI solutions depend on data that is accurate, complete, accessible and sufficiently structured to support the intended business use case.
However, data quality alone is not enough. Organisations also need clear data ownership, defined access rules, strong data protection controls and effective integration mechanisms. When relevant data is fragmented across systems or difficult to access, AI solutions are less likely to deliver consistent and reliable results.
The greatest value is often created when internal data is combined with relevant external or contextual information in a secure and controlled manner. This is especially important for use cases such as knowledge retrieval, customer service automation, predictive operations and risk detection, where broader context can significantly improve the quality and relevance of AI-driven insights and decisions.
AI adoption extends beyond technology – it changes processes, responsibilities and decision-making. Organisations need clearly defined roles, accountability structures and operating models before AI becomes part of daily operations.
A strong governance framework should define ownership of the AI solution, who approves new use cases, who monitors performance and who manages risks. This includes security, compliance, data protection, model behaviour, auditability and ongoing user adoption.
An innovation-friendly culture is equally important. Employees need to understand why AI is being introduced, how it affects their work and where it can help them become more effective. Without clear communication and change management, even technically successful AI solutions may fail to deliver their intended business value.
Damovo AI Solutions Portfolio
Damovo applies AI across five core solution areas: Unified Communications, Customer Experience, Enterprise Networks, Cybersecurity and Managed Services. Across each area, Damovo combines AI consulting, solution design, technology integration and ongoing optimisation to help organisations achieve measurable business outcomes.
Unified Communications
AI can enhance collaboration, improve access to organisational knowledge and streamline document-intensive workflows. Typical use cases include AI-assisted RFP evaluation, intelligent knowledge retrieval and sovereign collaboration environments where communication, data storage and AI processing remain under the organisation’s control.
Customer Experience
AI-powered voicebots, virtual assistants and agentic automation can reduce call volumes, improve routing accuracy and automate repetitive customer service requests. Use cases include citizen service automation, self-service capabilities and intelligent routing in large-scale contact centre environments.
Enterprise Networks
AI can strengthen network operations through automated monitoring, anomaly detection, root-cause analysis and proactive remediation. Common applications include predictive network operations, AI-assisted NetOps and performance optimisation across manufacturing sites, branch locations and global enterprise networks.
Cybersecurity
AI can help organisations identify and manage risky AI usage, test AI-enabled interfaces and reduce the risk of data exposure. Use cases include automated red-team testing of AI voicebots, AI security assessments and governance controls that address Shadow AI and compliance requirements in regulated environments.
Managed Services
Damovo is integrating AI capabilities into its managed services portfolio to support ongoing monitoring, optimisation and automation across all solution areas. This enables organisations to realise ongoing value from AI while reducing operational complexity and maintaining effective governance and control.
AI technologies Damovo works with
Damovo works with AI-enabled platforms from leading technology partners and combines them with integration, security, governance and managed service capabilities. Depending on the use case, solutions may include technologies from Cisco, Extreme Networks, Genesys, Microsoft, NiCE Cognigy, Parloa, Pexip, Rocket.Chat, Zoom and other specialist providers.
AI Use Cases
AI use cases show where artificial intelligence delivers real, measurable value in practice. They illustrate how organisations apply AI to specific challenges, from improving customer interactions to optimising operations, strengthening security and enabling better decision-making. The following examples give you a practical view of how AI can be applied, what it takes to implement it, and what results can be achieved across different business areas.
Sovereign AI collaboration for public services
Challenges
A public administration organisation in North Rhine-Westphalia with approximately 12,000 employees across departments, schools, and municipal services, faced a significant challenge. Due to GDPR requirements and data sovereignty obligations, sensitive citizen information and internal communications could not be transferred to public cloud environments.
Implementation
To address these requirements, the organisation implemented a fully on-premise collaboration platform built on Rocket.Chat and Pexip, enhanced with locally hosted AI capabilities. Large language models (LLMs) run entirely on local infrastructure through standard APIs, and a Retrieval-Augmented Generation (RAG) architecture securely connects AI services to internal knowledge repositories. As a result, all data processing, storage and AI interactions remain within the organisation’s own environment.
Results
The solution provides employees with modern collaboration and AI-powered knowledge access while maintaining complete control over sensitive information. By eliminating reliance on public cloud services, the organisation achieved full digital sovereignty without compromising usability, innovation or regulatory compliance.
Agentic AI routing and self-service for Insurer
Challenges
BarmeniaGothaer’s customer service teams were managing up to 6,000 calls per day, but complex routing structures, multiple contact numbers, and an underperforming IVR system led to significant operational challenges. Calls were being incorrectly routed, customers experienced longer wait times and employees spent valuable time manually forwarding calls and repeatedly identifying customers before enquiries could be addressed.
Implementation
Together with Damovo, BarmeniaGothaer implemented an agentic AI voicebot powered by Parloa, fully integrated with the Genesys Cloud contact centre platform. The solution handles natural-language interaction, automated customer identification, and intelligent call routing. All interaction data flows into Genesys Cloud for analysis, providing complete visibility into customer journeys and service performance.
Results
As a result, 89% of inquiries now route correctly without manual intervention. The switchboard handles more than 1,000 fewer calls per day. Customer satisfaction increased, reflected in a higher Net Promoter Score (NPS), and the solution is now being rolled out across the broader BarmeniaGothaer group.
Predictive network operations with Cisco AI in global manufacturing
Challenges
A global manufacturing company with approximately 5,800 employees across 13 sites was managing its networks infrastructure reactively. Manual processes, limited visibility, and time-consuming troubleshooting meant that network issues often impacted production before IT teams could act.
Implementation
To modernise operations, the company deployed Cisco’s AI-driven networking portfolio across all locations. Cisco AI Access provides identity-based access management for employees, partners, and devices, ensuring secure and consistent connectivity. Cisco AI Ops and Agentic Ops automate network monitoring, anomaly detection, root-cause analysis, and guided remediation, enabling teams to identify and address issues before they affect business operations Cisco AI Canvas delivers a unified workspace where telemetry, diagnostics, and AI-generated insights are consolidated, providing a comprehensive view of network performance across the organisation.
Results
As a result, the transformation enabled the company to move from a reactive network management to a predictive operating model. Incident resolution times fell, manual effort decreased, and network stability across production sites improved. IT teams gained greater visibility that enabled them to support AI-driven workloads without adding headcount.
AI Voicebot penetration testing at a global travel company
Challenges
A global travel company with approximately 50,000 employees used AI-powered voicebots to manage high volumes of customer interactions, including bookings, rebookings, refunds, and disruption management. While these systems delivered significant operational benefits, they also introduced a new attack surface that had not been systematically tested. Traditional penetration testing tools were not designed for telephone-based AI interactions, and manual red-team testing could not keep up with the volume.
Implementation
The company adopted an agent-based Voicebot Penetration Testing platform as a managed service to address this challenge. It automatically generates attack scenarios aligned with the OWASP GenAI Top 10, executes real attack calls against the production voicebot, records both sides of each conversation and transcribes interactions for analysis. An independent AI-based assessment engine evaluates outcomes, classifies findings by attack vector and severity, and produces structured reports with complete evidence trails.
Results
This approach enabled continuous and scalable security validation across hundreds of potential attack scenarios. The organisation gained visibility into risks specific to voice-based AI systems, including prompt injection via speech, automatic speech recognition (ASR) manipulation, social engineering attacks, and potential data leakage. Security gaps could be identified and addressed early, without disrupting live customer service.
Why work with Damovo on AI?
Damovo helps you turn AI ideas into working solutions by combining consulting, technology selection, integration and ongoing optimisation. We start with the use case, not the tool, and assess where AI can create measurable value in your organisation.
Because AI only works in practice when it fits your existing environment, we connect AI solutions with your communication platforms, contact centres, networks, security tools, knowledge bases and business applications. We also consider governance, data protection and compliance from the beginning, especially where security, digital sovereignty or regulatory requirements matter.
From first assessment to implementation and optimisation, Damovo supports you with the technical and operational expertise needed to make AI usable, secure and scalable.
Supporting organisations across multiple industries
Frequently asked questions about AI solutions
What does Damovo mean by AI solutions?
Damovo AI solutions are practical applications of artificial intelligence across customer experience, communications, enterprise networks, cybersecurity and managed services. They can include voicebots, AI agents, RAG-based knowledge access, AI-supported network operations, AI security controls and automation.
How will Damovo help us in identifying reasonable AI use cases?
We will conduct AI discovery workshops with all necessary stakeholders to identify best matching use cases, low hanging fruits while matching customers guardrails and particularities.
When should we use a custom AI solution?
A custom AI solution is useful when your use case depends on specific data, strict security requirements, complex workflows or integration with internal systems.
When should we use AI capabilities from an existing vendor platform?
Vendor-based AI is often the better choice when the required capability already exists in a trusted platform and can be configured, integrated and governed faster than building a bespoke solution.
What data is needed for AI projects?
AI projects need reliable, accessible and well-governed data. Data quality, ownership, structure, security and integration are usually more important than the AI model itself.
How can AI be used securely in regulated industries?
AI can be used securely when data access, model usage, auditability, compliance, identity management and human oversight are defined from the start. In some cases, sovereign or on-premises deployment models may be required.
How does Damovo support AI after implementation?
Damovo can support AI solutions after go-live through monitoring, optimisation, governance support, security controls and managed services.
Ready to identify your first AI use case?
The right AI project should be specific, measurable and realistic to implement. Damovo can help you assess use cases, validate the business value and define the right delivery model.